Scalable GNSS Performance Assessment and Environment Categorization Under Vegetation

Jiayi Zhou, Timothée Guillemaille, Cyril Meurie, Guohao Zhang, and Juliette Marais

Peer Reviewed

Abstract:	While GNSS performance can be significantly affected by environmental factors such as urban buildings and vegetation, a scalable correlation between environmental features and positioning performance still remains to be developed, particularly in complex environments containing both vegetation and buildings. In this study, dynamical GNSS datasets collected in diverse GNSS operating environments of both Hong Kong and France are assessed based on the categorization from environmental features and satellite reception states. In general, positioning performance degrades the most within Urban environments compared to Vegetation environments, while Vegetation/Urban Mixed environments are more complex due to high geometry dependency. At the measurement level, vegetation tends to introduce higher degradations at medium elevation angles while urban buildings tend to introduce more significant degradations at low elevation angles. Moreover, the potential environment classification parameters are discussed, in which the significant variations of measurement features from low-elevation angle satellites can indicate the presence of urban buildings, while the rapid fluctuation of pseudorange error and carrier to noise density ratio can be used to indicate the presence of vegetation. Keywords—GNSS, Multipath, Vegetation, Urban Areas, Environment Categorization.
Published in:	2025 IEEE/ION Position, Location and Navigation Symposium (PLANS) April 28 - 1, 2025 Salt Lake Marriott Downtown at City Creek Salt Lake City, UT
Pages:	1204 - 1215
Cite this article:	Zhou, Jiayi, Guillemaille, Timothée, Meurie, Cyril, Zhang, Guohao, Marais, Juliette, "Scalable GNSS Performance Assessment and Environment Categorization Under Vegetation," 2025 IEEE/ION Position, Location and Navigation Symposium (PLANS), Salt Lake City, UT, April 2025, pp. 1204-1215.
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